Zois Boukouvalas

Zois Boukouvalas is an Assistant Professor in the Mathematics and Statistics Department at American University. His research focuses on interpretable machine learning models for analyzing multi-modal data, integrating information geometry, statistics, and optimization. He applies these methods to biomedical imaging, social/linguistic trends, and chemical data for drug discovery. Boukouvalas holds a PhD in Applied Mathematics from UMBC (2017) and has received grants from the Energetics Technology Center for projects on machine learning in energetic materials and misinformation detection.

Education: BS in Mathematics (University of Patras, Greece), MS in Applied and Computational Mathematics (Rochester Institute of Technology), MS and PhD in Applied Mathematics (UMBC). His work bridges theoretical foundations with practical applications, emphasizing ethical AI and underrepresented student inclusion. Recent contributions include panel discussions on AI in energetic materials and co-organizing conferences on latent variable methods in machine learning.

Research interests span misinformation detection, multimodal learning, and algorithmic transparency. He has published extensively on topics like hate speech detection, stock market prediction via news data, and data fusion techniques for high-impact events. Awards highlight his innovation in human-assisted machine learning and materials discovery.